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jmcs()
.AUCjmcs()
area under the ROC curve
(AUC) to assess the prediction performance of joint models.surviftjmcs()
.plot.surviftjmcs()
due to theoretical
problem.PEjmcs()
and MAEQjmcs()
.surviftjmcs()
for the competing risk and add
summary()
for providing parameter estimates and SE for both
sub-models.jmcs()
.Provide support for handling categorical variables in both sub-models.
Provide the anova()
function to compare two fitted
joint models.
Add the simulate
argument in the
survfitjmcs()
function to obtain the conditional
probabilities using the Gauss-Hermite quadrature rule for numerical
integration.
Adjust the label position of y axis for clarity purposes when
include.y = TRUE
in the plot.survfitjmcs()
function.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.